If f(x) = 3x + 1, what is one possible value of x for<br> which 6

Fifteen more than twice a number 9 is

Leokris [45]

The question is asking 15+2*9=? so to do this first multiply 2*9 which = 18 and then add 15, 18+15= 33

6 0

3 years ago

Read 2 more answers

Rosetta drove her car 160 miles in 2 hours. Which of the following correctly expresses her rate as a ratio?

svp [43]

Rosetta drove her car 80 miles in one hour. Assuming that you ate talking about miles per hour you decide 160÷2=80 therefore, she drove 80 miles per hour (80/mph)

7 0

3 years ago

Read 2 more answers

it is known that the population proton of utha residnet that are members of the church of jesus christ 0l6 suppose a random samp

Lady_Fox [76]

Answer:

0.0838 = 8.38% probability of obtaining a sample proportion less than 0.5.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution

Problems of normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean $\mu = p$ and standard deviation $s = \sqrt{\frac{p(1-p)}{n}}$